Workflow Hub

I love reading, but my Goodreads TBR list keeps growing faster than I can get through it. After years of adding books, I had forgotten what was actually there and was wasting time scrolling whenever I needed to choose my next read. I asked Claude to analyze my reading history, 4-star and 5-star ratings, author patterns, and recurring genres to create a profile of my book personality. I then used that profile to select and rank the next 50 books from my TBR list. Because I enjoy matching books to the season, I also asked Claude to organize the recommendations by seasonal feel. Now I have a reference list that makes it easier to decide what to read next. Step-by-step: 1. I exported my library data from Goodreads. 2. I uploaded two files to Claude: my Read list and my To-Be-Read list. 3. I used the prompt below to generate a personalized reading roadmap based on my book personality. 4. I used the resulting list as a reference so I would not spend as much time deciding what to read next. PROMPT I have attached two CSV files: 1. My Read list. 2. My To-Be-Read list. Create my book personality profile by analyzing my reading history, my 4-star and 5-star ratings, author patterns, and recurring genres. Using that data, filter and rank my To-Be-Read list to build a personalized roadmap of the next 50 books I should read from that list. Evenly organize recommendations into 4 seasonal blocks based on these descriptions: - Autumn: Atmospheric, moody, or suspenseful books with a cozy but engaging pace. - Winter: Immersive, complex, or cozy books (slower burns or dense world-building). - Spring: Fresh, character-driven, or thought-provoking fiction with a steady pace. - Summer: High-energy, immersive, or breezy books (fast-paced page-turners). Next, generate an interactive digital bookshelf widget to display the recommended books. The design should feature: - A warm, premium editorial color palette. - Minimalist style cards for each book displaying Title, Author, and color-coded genre badge. - Interactive navigation tabs at the top to toggle between the four seasons. - A functional "Mark as Read" click action on each card that visually updates a progress ring or tracker.

I built a workflow in which AI agents build a product under the kind of governance I’d expect from a senior engineering team. After 20 years in software engineering, including 10 years running my own businesses, I learned that writing code isn’t the hard part. Keeping control of it is. So I set up a build loop on GitHub. One thing I’ve kept for myself is decision-making: approving plans, answering product questions, and signing off on what the system has learned. The product it’s building is Cordexa, a tool for document-heavy expert work, such as quantity take-offs from engineering drawings or assessing a tender against a company’s own CVs and case studies. It’s designed to run inside the customer’s own cloud, so their documents never leave it. Every figure is cited to the document it came from or flagged for a person. It never guesses. The problem it solves is complex, but the build loop lets the system work in the background and notify my phone when it needs a decision. That allows me to stay focused on my day job. Step-by-step: 1. I start every module with a plain-English plan and approve it before any code exists. 2. One agent writes the tests from the plan. A different agent writes the code, and it cannot modify the tests. 3. An independent reviewer agent checks every change against its plan. Safe changes merge automatically; anything that changes the product waits for my approval. 4. A watchdog checks every 15 minutes for anything that is stuck, fixes it when possible, or tells me why it cannot. 5. Real test jobs run after every merge and are scored against known answers, so progress is measured rather than guessed. 6. I handle the decisions, answer product questions, and sign off on what the system has learned.

I built a custom GPT with the personalities of Einstein, Lorentz, Planck, and Compton. Starting with Einstein’s 1920 Leiden lecture, I developed an ontology for physical space based on his description of the “new ether.” I began with E=mc2, E=hf, and my ontological modeling assumptions. I then repeatedly pushed ChatGPT to challenge those assumptions and interpretations logically and mathematically. We used numerous tool calls and reference sites to write and test the math. After many months of working on it in my spare time, the GPT now unifies equations that balance from the atomic scale to the black hole scale, with some remarkable revelations. I summarized much of the work in a paper written by my GPT and am happy to share it so others can expand, improve, and test the work. AI rocks. Step-by-step: 1. I built a custom GPT with the personalities of Einstein, Lorentz, Planck, and Compton. 2. I used Einstein’s 1920 Leiden lecture and his description of the “new ether” to develop an ontology for physical space. 3. I started with E=mc2, E=hf, and my ontological modeling assumptions. 4. I repeatedly challenged the assumptions and interpretations with ChatGPT, focusing on logical and mathematical consistency. 5. I used numerous tool calls and reference sites to write and test the math. 6. After many months of working on the project in my spare time, I summarized much of it in a paper written by my GPT. 7. I am sharing the work so others can expand, improve, and test it.

I 3D-printed a custom threaded barrel nut without ever opening AutoCAD. I use my 3D printer to make parts for various projects, including a pinball machine and a boat, but I struggle with AutoCAD. As an alternative, I described the part to Claude in plain English and asked it to create a `.3mf` file. I opened the file in Bambu Studio and printed the part without using AutoCAD. Step-by-step: 1. I described the custom threaded barrel nut to Claude in plain English. 2. I asked Claude to create a `.3mf` file for the part. 3. I opened the file in Bambu Studio. 4. I printed the part on my 3D printer.

I run Mealie, a self-hosted recipe manager, in Docker on a home server. Mealie is good, but it isn't zero-effort: there's a learning curve and ongoing administration just to keep a recipe library populated. My wife is Spanish, so many of her recipes are in her native language. We also have recipes as handwritten notes, WhatsApp messages, photos, and transcribed cooking videos. Translating the Spanish recipes accurately was beyond my ability, and I had no interest in typing everything into Mealie's editor by hand. I built a custom MCP server that exposes Mealie's API to Claude as a set of tools, along with a project-level skill that encodes our household rules: no mushrooms or celery for me, no ginger or coriander for my wife, a child who is fussy about fish, a pizza oven that only comes out at weekends, and batch cooking that needs to cover school lunches. Now I can give Claude a batch of recipes in whatever form they arrive and in any language. It translates them, restructures them into Mealie's schema, checks for duplicates, and writes them straight into the database. There's no manual entry, and I've never needed Mealie's own foodstuffs database. Ingredients stay as plain text, which is one less thing to maintain. For weekly planning, Claude reads recent meal-plan history from Mealie so that meals do not repeat too soon. It pulls the full ingredients for every candidate recipe rather than trusting the recipe name—a "Veggie Traybake" might contain mushrooms—and drafts the week against our constraints. The plan covers seven family dinners, five weekday lunches for the two of us, and two weekend family lunches. School lunches for the kids are often something we've batch-cooked, so that is built in too. Nothing is written to Mealie until I confirm the draft. After I confirm it, Claude seeds the plan into Mealie, merges ingredients across every meal, rounds them to sensible pack sizes, and groups everything by supermarket aisle. The output feeds through to Home Assistant, so the weekly plan and shopping list appear on our wall-mounted display and mobile phones. Step-by-step: 1. Create a self-hosted instance of Mealie in Docker on a home server. 2. Build a custom MCP server that exposes Mealie's API to Claude. 3. Create a project-level skill that encodes our dietary preferences, cooking setup, and usual meal structure. 4. Batch-feed Claude recipes in any format and language, including photos, WhatsApp messages, video transcripts, and handwritten notes. 5. Skip Mealie's structured foodstuffs database and keep ingredients as plain text while Claude parses them into Mealie's recipe schema. 6. Ask Claude to translate recipes, check for duplicates, and write the structured recipes into Mealie. 7. Ask Claude to read recent meal-plan history, check candidate ingredients against our preferences, and draft a new weekly plan covering our required dinners and lunches. 8. Make changes through chat, and confirm the draft before anything is written to Mealie. 9. Have Claude seed the confirmed plan into Mealie, merge ingredients across every meal, round them to sensible pack sizes, and create a categorised shopping list. 10. Install the Mealie integration in Home Assistant to display the plan, recipes, and shopping list on your devices.

An artist portfolio can feel harder to make than the work itself. My work lives across years, folders, and materials: photographs, drawings, collage, objects, costumes, and digital experiments. Then an application asks me to explain who I am, what I make, and where I want to go. I used AI to help assemble a readable portfolio for HUP03, a Kunstwerkt call. The portfolio included eight works spanning photography, collage, assemblage, performance, and AI-assisted illustration. The goal was to organise the material without flattening it into a generic artist statement. Step-by-step: 1. I started with the actual work: images, titles, dates, and any explanations I already had. For each piece, I established the title, year, medium, whether it was a standalone work or part of a series, what prompted it, how it was made, and which details mattered. I gave AI concrete material rather than asking it to invent my practice. 2. I described each piece in my own words, as I would to someone looking at it beside me. One of my works, *Olifant onder het tapijt*, combined three expressions: the elephant in the room, the elephant in a china shop, and sweeping things under the carpet. The setting became a recognisably Flemish grandmother’s living room, with floral wallpaper, a crucifix, and a decorative cow painting. Those details carry the humour and the observation about hiding uncomfortable things. “An exploration of concealment” would lose much of what makes the piece mine. I asked AI to organise my explanation while preserving its specific ingredients. 3. I gave each work a clear, consistent entry with the title, year, and medium; a short description of the idea; and the relevant visual or material choices. Consistency makes varied work easier to read, even though a photograph, costume, and assemblage may need different explanations. Where relevant, I stated AI involvement clearly, such as “digital illustration developed in collaboration with generative AI.” 4. I selected work for range and relevance. My selection included an older photograph, a costume/performance, collage, recent assemblages, and digital work. I asked what each piece contributed: another method, question, period, or way of seeing. I avoided choosing only the newest work or forcing everything into one visual style. The task was to make the selection understandable. 5. I separated the existing artwork from the proposed project. Existing works show what I have made, while a project proposal explains what I want to investigate next. For this application, my project question centred on human connection: a thread becoming a web, then a garden. AI helped articulate that direction without forcing every earlier piece into the same project. I looked for connections I recognised while leaving room for differences and discoveries. 6. I wrote an accurate artist introduction describing the media I use, what interests me, and my current practice. I checked every biographical claim and distinguished courses attended, qualifications completed, and current activities. In my case, wording around an interior design course needed care so it would not imply that I had completed the programme. AI can make a biography sound polished while quietly making it less true, so I read the facts as closely as the prose. 7. I treated the first draft as material to revise. Useful feedback included: “That sounds more formal than I mean,” “This detail is essential,” “You have made the idea too abstract,” “That suggests a qualification I do not have,” and “This is my observation, not a universal claim.” AI revised the structure and wording, but I decided whether the text still belonged to the work. 8. I checked the portfolio as a whole against the application requirements. I reviewed titles, dates, media, image-text matches, requested sections, and length limits. I removed repetition between the artwork descriptions, biography, and proposal. Someone unfamiliar with me should be able to understand the work without needing the conversation behind the portfolio. The overall process was: gather existing work, explain each piece, verify the facts, build consistent entries, select a meaningful range, describe the proposed direction, revise in my own voice, and check the application. AI was useful as an interviewer, organiser, and editor. The substance came from the work and the person who made it. A portfolio does not need to turn a varied practice into one tidy slogan; it needs to give someone a way into it.

I use a persistent AI project-orchestration workflow for small-business operations, internal tools, AI-assisted coding, specialist-agent workflows, and projects that need to survive fresh sessions. A root AI receives one bounded objective, reads the project state from files, breaks the work into phases, and delegates implementation to bounded specialist or worker agents. It verifies the results, updates the durable state, and continues until the acceptance criteria are met. This lets me run small-business and coding projects end to end without re-explaining the project every session or manually driving every handoff. The core pattern is: Objective → durable state → root orchestrator → bounded workers → verification → state update → next step → Git checkpoint. Step-by-step: 1. I give the root AI one bounded objective. 2. The root AI reads the current project state from files. 3. It breaks the work into phases and delegates implementation to bounded specialist or worker agents. 4. It verifies the workers’ results. 5. It updates the durable project state. 6. It moves to the next step and creates a Git checkpoint. 7. It continues this cycle until the acceptance criteria are met.

We’re narrowing down high school options for my daughter, who wants to focus on art. I’m using Astra to find public schools in our area and beyond with strong art programs and understand how they compare. Astra can research school rankings and academic results, review art courses and facilities, and bring admissions requirements, portfolio expectations, fees, and commute times into one clear comparison. It can also suggest preparation classes—such as drawing, painting, digital art, or portfolio workshops—based on what each school expects. From there, it’s helping me build a shortlist, organize open days and application deadlines, and prepare questions for school visits. We can work through the options together and find a school she’s excited about. Step-by-step: 1. I use Astra to find public schools in our area and beyond that have strong art programs. 2. I ask Astra to research each school’s rankings, academic results, art courses, and facilities. 3. I bring admissions requirements, portfolio expectations, fees, and commute times into one comparison. 4. I use each school’s expectations to identify helpful preparation classes, such as drawing, painting, digital art, or portfolio workshops. 5. I use the comparison to build a shortlist and organize open days and application deadlines. 6. I prepare questions for school visits so my daughter and I can work through the options together.

I used AI to reconstruct more than two decades of professional work scattered across old publications, broken links, archives, indexes, and surviving records. The difficult part was not finding possible matches; it was preventing AI from turning incomplete evidence into false certainty. I built a workflow that treats archive reconstruction as a verification problem rather than a search problem. The process separates verified work from probable matches, resolves same-name conflicts, deduplicates reprints and syndicated copies, preserves source provenance, and treats missing years as research gaps rather than permission to invent records. The result was a source-tracked professional archive containing 250+ recovered records across more than 20 years. The workflow can be adapted to journalism, research, consulting, creative work, speaking histories, or any long-term body of professional output. I use the following research-archivist workflow: - Never invent titles, dates, publications, URLs, or authorship. - Separate records into VERIFIED, PROBABLE, and REJECTED. - Treat same-name individuals as separate identities until evidence proves otherwise. - Deduplicate syndicated copies, reprints, mirrors, indexes, and cached pages. - Preserve provenance for every verified record. - Mark gaps rather than filling them. - Distinguish “not found,” “unverified,” and “confirmed absent.” - State uncertainty explicitly. Step-by-step: 1. Ask for names and bylines, employers, publications, career dates, subject areas, known titles, source files, URLs, and any same-name conflicts. 2. Build an identity fingerprint. 3. Establish verified seed records. 4. Search iteratively across multiple query patterns and sources. 5. Classify every result. 6. Resolve identity conflicts. 7. Deduplicate records. 8. Normalize metadata. 9. Build a chronology. 10. Identify and search gaps. 11. Run a provenance and error audit. The final output includes: - Verified Master Archive - Probable / Needs Verification - Rejected / Other Person - Gap List - Source Notes - Final Audit The archive is never described as complete unless the evidence supports that conclusion.

My partner and I were tired parents trying to help our child sleep better. We had spent hundreds of euros on expert services and courses, but because every child is different, we still hadn't found an approach that worked consistently. For months, we had talked about trying to improve her sleep. She was waking us up multiple times each night, so we used Gemini to develop a plan with stages. In the first stage, we helped her start sleeping in her own room, which she achieved within a week. The second stage focused on getting her to sleep in her bed for the whole night, and we saw great success after the first night. The game changer was having immediate help when things didn't go as planned. At 2 a.m., I described situations such as my child lying on the floor and refusing to move, asking me to stay, wanting to cuddle or be carried, waking up her sibling, or coming back to our room again. Gemini adjusted the plan and told my tired brain what to do next. My child came back from her room 10 times, and each time I described the situation to Gemini and received practical advice. She eventually slept through the night and woke up in the morning feeling proud and happy. To me, that showed that the techniques Gemini suggested had helped create a safe space for sleep. Step-by-step: 1. I described our child's sleep challenges and the approaches we had already tried, including expert services and courses. 2. I asked Gemini to develop a staged plan tailored to her individual needs. 3. I followed the first stage until she was sleeping in her own room, which took one week. 4. I moved on to the second stage: helping her sleep in her bed for the entire night. 5. When unexpected situations came up during the night, I described them to Gemini in real time. 6. I followed the adjusted advice each time she got out of bed, including when she returned 10 times. 7. I continued until she slept through the night and woke up proud and happy.

I had a ChatGPT subscription that I knew I would not use during the coming month, so I wanted to find a productive use for the tokens I would otherwise pay for without using. I created a team of specialized agents on Raft that works persistently in the background. They continue working until all my allotted tokens are used, wait for the tokens to reset, and then continue. I assigned them a project: searching a stretch of LiDAR-mapped dense forest in Slovenia for archaeological discoveries. The agents scan the dataset for meaningful anomalies that could point to potential findings. Anything they identify will be handed over to the Slovenian authorities for further investigation. Even if they find nothing, that result is valuable in its own right. I encourage anyone with an idle AI subscription to use it for something beneficial. There are many publicly available datasets containing potential discoveries that no human would ever have time to examine manually. Step-by-step: 1. I identified a ChatGPT subscription that I would not be using during the coming month. 2. I created a team of specialized agents on Raft to work persistently in the background. 3. I configured the team to work until my allotted tokens were used, wait for the reset, and then continue. 4. I directed the agents to scan LiDAR data from a dense forest in Slovenia for meaningful anomalies. 5. I planned to hand any potential discoveries over to the Slovenian authorities for further investigation. 6. I treated a lack of findings as valuable information as well, since the dataset would still have been examined.

I spent twenty years producing documentary and nonfiction films, all of it the expensive way: crews, schedules, financing, and commissioners saying no. I'm proud of much of that work, and I still think about the films that never got made because the money wasn't there. Generative video pulled me in, but not for the reason people assume. It wasn't the speed or the cost. It was the fact that I could finally finish something without asking anyone for permission. No green light from a major platform. Nobody deciding that a story was too small to deserve a crew. A year later, I could produce this way. It turned out to be a different job rather than the same job with new tools. Then I ran into a problem I hadn't considered: how do you show the work? A finished shot doesn't prove much anymore. A client can look at a beautiful frame and have no way to tell whether it took three weeks or three minutes. If I'm honest, neither would I in their position. The thing every portfolio is built to display had stopped being evidence. I'm not a developer. I produce films. The site is bilingual, has seven sections, includes a case study for each film, plays video on hover, and has a comparison slider that works with a thumb as well as a mouse. I built all of it. I'd wanted this exact site for about fifteen years and had never managed to explain it properly to anyone I paid to make it. This time, I stopped explaining and built it myself. The middle of the page has a slider that you drag between the storyboard panel and the final shot. A basic before-and-after would prove nothing; you can fake that by generating twice. The storyboard is the proof because the framing, eyeline, and decision about what stays outside the frame all existed on paper before any model was asked to generate anything. You drag the handle and watch the intention survive. The rest of the site came from the same place. Video only plays on hover and is silent until you click for sound. Most video portfolios are unreadable because fifteen things start moving at once. The sections are organized by register rather than by client: epic, brand, animation, and lifestyle. A wall of logos answers who has hired you, which nobody is actually wondering. What they want to know is whether you can change tone. Nothing is cropped to fit the grid. A 5:33 film sits in 16:9 next to a 2:42 film in scope, and the layout is lopsided because of it. I nearly made them uniform because it looked tidier, then realized I was about to reformat my own films so a webpage would look neat. Anyone who cuts for a living spots that immediately. Concepts are labeled as concepts. Experiments have their own section instead of being scattered among the client work and hoping nobody asks which is which. Step-by-step: 1. I started from what my client couldn't verify: the craft behind a shot. 2. I found the artifact that proves it. It's usually the ugly thing nobody publishes: the storyboard, the reference sheet, or the version before the good version. 3. I put that artifact next to the finished piece inside one interaction, so nobody has to search for the proof. 4. I made playback deliberate. Video plays on hover, and sound requires a click. 5. I sorted the work by the question being asked rather than by the credentials I wanted to lead with. 6. I didn't reformat the work to fit the layout. I let the grid be uneven. 7. I labeled everything honestly: client, concept, or experiment. One word each can pay for the credibility of the whole page. 8. I wrote a brief for each section as a document first. Arguing with a paragraph is free; arguing with a built page is not. 9. I built the site with an AI coding tool. This was the least interesting step, even though it's the one everyone writes about. If your work can be mistaken for a lucky prompt, stop trying to prove it with the finished piece. The site is at www.termopilas.tv if you want to drag the handle yourself.

I built a workflow that converts landscape (16:9) footage into vertical (9:16) video without heavily cropping the original footage. Using AI tools and a simple node tree in Magnific, I generated the missing visual data outside the original frame so the final video looks as if it was captured vertically. Previously, this felt like too much trouble and too many steps to be worthwhile. Using Magnific Spaces, I extended the first frame of the video to fit a 9:16 frame with one prompt. Then I used a second prompt with the new frame and the original video as references in a video-generation node. I used Seedance 2.0 to save credits because I had low expectations that the workflow would work. To my surprise, the output was great immediately. It was not perfect, but the generated footage matched the original clip very well. Below are images of my node tree. Step-by-step: 1. I opened the original landscape (16:9) video in Magnific. 2. I used one prompt to extend the video's first frame into a 9:16 vertical frame. 3. I added the new frame and the original video as references in a video-generation node. 4. I used a second prompt to generate the expanded vertical footage with Seedance 2.0. 5. I reviewed the output and found that it matched the original clip well, although it was not perfect.

Spotify can tell me what I played, but it rarely explains what music does in my life. I use exported listening data, playlists, and personal context to explore taste, memory, regulation, identity, and continuity. I look at both observable listening behavior and the meanings I attach to it, without forcing every track into a biography. Step-by-step: 1. Start with the data. I export streaming history, tracks, artists, timestamps, listening duration, saved songs, and playlists where available. I check the period covered and remember that missing history does not mean a song never mattered. I start with behavior: a supposed favorite may barely appear, while another track may have played hundreds of times quietly. 2. Separate tracks from artists. I count unique tracks, unique artists, and tracks per artist. One artist with dozens of songs and hundreds of artists represented by one track suggest different listening styles: exploring discographies versus collecting exceptional songs. 3. Find lasting favorites. I distinguish temporary high-volume artists from favorites that return across years, playlists, and life phases. I look for persistent songs: tracks spanning years, returning after long gaps, or appearing in several playlists. Persistence can matter more than total plays. Some songs never win a monthly ranking yet refuse to disappear. 4. Connect music to life. I add relevant personal context, such as teenage years, university, a move, a relationship, or rebuilding. I ask which songs became landmarks and which were described as “my song.” Dates show when music appeared; I explain what that timing means instead of forcing every track into a biography. 5. Discover playlist functions. Repeated songs are not always favorites. Sometimes music has a job: providing strength, calm, concentration, singing, emotional release, or a way to reconnect with myself. I read playlist names alongside their tracks and listening patterns. “Power” might mean, “I do not feel strong, so I need external strength.” “Gold” might mean, “I want to feel my own golden heart again.” These meanings matter more than a generic genre label. 6. Treat corrections as evidence. AI suggests interpretations, but I decide whether they fit. For example: “This song is not nostalgic. It reminds me that I remain myself regardless of what another person does.” Corrections like this can change the meaning of an entire playlist, so I keep them alongside the data. 7. Look for regulation patterns. I find songs played daily for weeks, tracks repeated within one day, and playlists used at consistent times. I ask whether they accompany difficult tasks, low energy, singing, or calming down. Repetition is a clue, not proof of an emotional function. 8. Look beyond genre. Pop, rock, electronic music, mantra, and soundtracks can share dramatic builds, layered vocals, melodic hooks, powerful voices, intimate production, or mantra-like repetition. These qualities can explain why apparently unrelated songs belong together. I let myself name the patterns; “Post-Classical Electroacoustic Intimate Futurism” may be more useful for discovery than a standard genre. 9. Separate nostalgia from continuity. An old song returning is not necessarily nostalgia. I ask whether it is remembered or still actively used. Some music belongs to the past, while some music simply has a very long present. I keep different musical worlds separate: anthemic pop and meditative mantra may serve different parts of the same person. 10. Build a traceable profile. I include core artists, persistent songs, personal anthems, life-phase associations, functional playlists, regulation patterns, musical qualities, and unexpected findings. I link interpretations to dates, play counts, playlist appearances, and my explanations. I separate observed behavior, AI hypotheses, and confirmed meanings, and keep the profile easy to correct. 11. Recommend for the person. Instead of asking for “songs similar to Coldplay,” I ask: “Find the emotional build I like in Coldplay, the vocal force I like in Sia, and the intimate electronic texture in my meditation playlists.” I can also ask: “Find tracks for Power that could survive repeated listening without copying its existing artists.” 12. Update over time. I add new data and look for new favorites, returning anchors, changing playlist functions, and stable patterns. The ongoing cycle is: export → analyse → find persistent songs → map functions → add personal context and corrections → build the profile → recommend → update. Listening history records what I played. AI helps me explore why something kept returning. I remain the only person who can finally say: “Yes. That is what this song means to me.”

I’m a nurse with no tech experience, but I’m curious and have many creative ideas. I used Claude to build a website that helps people plan which vegetables to grow based on their location, available space, and the time they have. Veggiegrowguide.com Step-by-step: 1. I identified an idea for helping people choose vegetables to grow based on their location, available space, and available time. 2. I used Claude to help me build a website for the idea. 3. I created Veggiegrowguide.com as a resource for people planning their vegetable gardens.

Today's Rundown reported that Anthropic spent the past year consulting religious scholars from different faiths to help shape Claude's morals. I use a different approach for Bible study, research, and writing Bible study books with Claude. I wrote a set of Bible study principles that Claude reads before any work begins and follows throughout the process. The main tenets are: - The Bible is the sole authority. No commentary, tradition, or scholar outranks the text. - Scripture interprets Scripture. When a passage is unclear, the answer comes from another passage, not from a theological system. - First ask what the text actually says before asking what it means. - Ask who is speaking, to whom, and under what circumstances. - Quote every verse word for word. Never paraphrase it. - Separate what the text states from what we infer, and say "I don't know" where the Bible is silent. The difference shows in the outcome. The books contain no man-made doctrines, denominational vocabulary, or creeds—only what the Bible says, with the passage shown for every claim. When a draft goes beyond the text, these principles catch it. My experience is that the moral standard an AI needs is already written down, and an AI can be held to it. The principles, along with 23 free books and studies, are available at noblemind.study. Step-by-step: 1. I wrote a set of Bible study principles for my own Bible study, research, and Bible study books. 2. I have Claude read the principles before any work begins. 3. I require Claude to follow the principles throughout the work. 4. I use the principles to keep claims grounded in the biblical text, quote verses word for word, and distinguish statements from inferences. 5. I review drafts for man-made doctrines, denominational vocabulary, creeds, and claims that go beyond the text.

I develop the song with ChatGPT, working through the lyrics, structure, mood, and sound tags. I then move the concept into Suno, which generates the music. Once the track is finished, my ChatGPT agent handles the download and archiving workflow where site authentication allows it. ChatGPT also creates the visual artwork. I assemble the finished song and image into a full video and a Short in Clipchamp. From there, I publish the content to YouTube and TikTok, then share the release across Facebook, LinkedIn, and X. What makes this workflow interesting is not any single AI tool, but the handoff between them: writing, music generation, file handling, image creation, video assembly, publishing, and distribution—all directed by one human creative process. The result is a practical, end-to-end AI-assisted production pipeline rather than a single AI-generated output. Step-by-step: 1. I work with ChatGPT to develop the lyrics, structure, mood, and sound tags. 2. I move the concept into Suno to generate the music. 3. Once the track is finished, my ChatGPT agent downloads and archives it where site authentication allows. 4. I use ChatGPT to create the visual artwork. 5. I combine the finished song and image in Clipchamp to create a full video and a Short. 6. I publish the finished content to YouTube and TikTok. 7. I share the release across Facebook, LinkedIn, and X.

I built a fantasy hockey assistant with Claude Code for my Yahoo league. It supports two parts of the league: running our in-person draft and managing my team during the season. For the draft, it runs on my laptop as a live draft board. I enter each pick as it is announced, and the assistant re-ranks the remaining players based on what my roster still needs. The rankings combine projection sources with three seasons of NHL statistics, and each player is scored according to how much they would help in each of our ten categories. The board flags players who will likely be selected before my next pick, identifies injury and regression risks, and opens a detail card for any player with projections, historical data, and a short plain-English assessment. A separate display goes on the TV for the room, showing the pick clock, the next player on deck, and recent picks. After the final pick, the view switches to projected standings for every team, color-coded by category. During the season, I paste in my roster, and the assistant pulls the NHL schedule, starting-goalie reports, and injury news. It suggests the best lineup for each day, ranks free agents by value and by games played that week, and projects my weekly head-to-head matchup category by category. It can also ask Claude to propose a lineup and explain its recommendations, which costs about ten cents per run in API credits. Yahoo’s API is read-only. If it supported write access, I would probably have the app manage my team autonomously. Step-by-step: 1. I built the fantasy hockey assistant with Claude Code for my Yahoo league. 2. During the in-person draft, I run the assistant on my laptop as a live draft board and enter each pick as it is announced. 3. The assistant re-ranks the remaining players based on my roster’s needs, using projection sources, three seasons of NHL statistics, and each player’s value across our ten categories. 4. I use the board’s flags and player detail cards to evaluate likely pre-next-pick selections, injury and regression risks, projections, historical data, and plain-English player assessments. 5. I display the pick clock, next player on deck, and recent picks on a separate TV view, then switch that view to color-coded projected standings by category after the final pick. 6. During the season, I paste in my roster so the assistant can pull the NHL schedule, starting-goalie reports, and injury news. 7. I use that information to set daily lineups, rank free agents by value and weekly games played, and project my head-to-head matchup category by category. 8. When needed, I ask Claude to propose a lineup and explain its recommendations, at a cost of about ten cents per run in API credits. 9. Because Yahoo’s API is read-only, I currently use the assistant for recommendations rather than autonomous team management.

For years, my family has run a shared birthday fund: five of us contribute a fixed amount for each birthday, while the person whose immediate family is celebrating that month is exempt from paying. Tracking everything in a payment app and a chat thread meant nobody knew the balance, who was behind, or what the next gift would cost. I rebuilt the fund as a small web app with Claude. The app itself isn't the only reason I'm writing this up. The same build is also the demo I use to teach clients and students how AI-assisted product development actually works, including the parts that go wrong. Step-by-step: 1. I described the real rules instead of presenting Claude with a generic app idea: the contributors, the fixed amount per person, the family-exemption rule, and birthdays with birth years so ages could be calculated automatically. Claude built the app as a single, self-contained HTML file with no build step or server, so it opens in any browser. 2. I worked in phases and asked Claude to explain its reasoning at each stage. Instead of using one giant prompt, I added one layer per session: the calculation engine, the setup screen, then reports and CSV export. The explanations made the sessions reusable as teaching material. 3. I required the app to be configured through its own interface rather than by editing code. This was the turning point. Claude removed the hardcoded demo family and added a full setup screen for the group name, contributors, deposits, birthdays, amounts, and alerts. I can now build a family from scratch live in front of a class in about [X] minutes without showing a line of code. 4. I let a data-loss incident shape the next phase. I entered the real family data, then the browser tab closed and the download link I had been using to open the file broke. The data was stored in browser storage tied to that exact URL, and I had no backup. Nothing was recoverable at the time. 5. I turned that failure into features. Claude added CSV export for both reports, CSV import that automatically detects which file it is reading, and a backup reminder that appears in the app's alert banner when the data has never been backed up or has not been backed up for seven days. We also discovered that birth year was missing from the export, which meant a re-import would have silently lost everyone's age. 6. I had Claude test its own work. Before each handoff, it ran a jsdom test suite in a sandbox. By the end, the suite had 46 tests covering the exemption math, empty states, CSV round-trips, and backup logic. Several real bugs surfaced there instead of in front of a class. 7. I made a second version for a different audience. One prompt produced a fully English, left-to-right translation with flipped directional CSS, Latin typography, US date formatting, dollars instead of shekels, and Venmo and Zelle instead of local payment apps. It was a genuinely different build, not a find-and-replace translation. The project took 14 sessions over one week and six hours total. It had no hosting cost, dependencies, or accounts. The data still lives in the browser's local storage on each device. Opening the file on my phone and laptop creates two unrelated pools, so CSV import is the manual bridge between them. There is no authentication or sync; this is a personal record-keeper, not shared infrastructure. The data-loss incident was not a Claude failure. I failed to build a backup path before entering real data. If I did it again, I would add export before adding a single feature. The highest-leverage prompt in the whole project was not a feature request. It was: "let me configure this through the interface instead of the code." That shift turned a static demo into something my family actually uses and my students can watch being built from an empty screen. The broader point is that the failure was the most useful part of the project. A polished demo teaches people that AI makes building easy. Losing the data and rebuilding the safety net around it teaches them what building actually involves—and that is the lesson that survives the workshop.

I asked ChatGPT to create a graphic for my company. I provided our company colors, logo, brand guide, the objective of the graphic, and information about our audience. ChatGPT then created the graphic based on those details. Step-by-step: 1. I asked ChatGPT to create a graphic for my company. 2. I provided our company colors, logo, and brand guide. 3. I explained the objective of the graphic. 4. I described the intended audience. 5. ChatGPT created the graphic using the information I provided.